Cardiac signal display and event detection using multiresolution z-score transform

ABSTRACT

A system comprising a medical device including a processor and a Z-score transformation (ZST) module. The system also includes a display in communication with the processor. The processor is adapted to receive sensor data obtained from at least first and second sensors adapted to produce a time-varying physiologic electrical sensor signal. At least one of the first and second sensors is implantable. The ZST module calculates a ZST for the sensor data received from the first sensor and a ZST for the sensor data received from the second sensor. The display is adapted to display the ZSTs in visual correspondence with each other over time.

CROSS REFERENCE TO RELATED APPLICATIONS

This patent application is a divisional application of U.S. patentapplication Ser. No. 11/459,588 filed Jul. 24, 2006. The specificationof which is incorporated herein by reference in its entirety.

TECHNICAL FIELD

The field generally relates to implantable medical devices and, inparticular, but not by way of limitation, to systems and methods forinterpreting data collected from physiologic sensors.

BACKGROUND

Implantable medical devices (IMDs) are devices designed to be implantedinto a patient. Some examples of these devices include cardiac functionmanagement (CFM) devices such as implantable pacemakers, implantablecardioverter defibrillators (ICDs), cardiac resynchronization devices,and devices that include a combination of such capabilities. The devicesare typically used to treat patients using electrical or other therapyand to aid a physician or caregiver in patient diagnosis throughinternal monitoring of a patient's condition. The devices may includeone or more electrodes in communication with sense amplifiers to monitorelectrical heart activity within a patient, and often include one ormore sensors to monitor one or more other internal patient parameters.In general, the sensors convert sensed internal parameters intoelectrical signals. The electrical signals monitored within the patientand the electrical signals from the sensors can be quantized byanalog-to-digital converters and stored in the IMD as data. Otherexamples of implantable medical devices include implantable diagnosticdevices, implantable insulin pumps, devices implanted to administerdrugs to a patient, or implantable devices with neural stimulationcapability.

IMDs are able to communicate with external devices using wirelesscommunication methods. The external devices are often externalprogrammers that use wireless communication links to change performanceparameters in the implantable device. The IMD also wirelessly transmitsstored data obtained from sensors to an external device. The externaldevice may then display the collected data on a computer screen displayor a strip chart recorder.

As technology used in IMDs advances, the devices are able to collectdata from multiple sensors at multiple locations. They also detectevents occurring from such multiple sources. Because the data may becollected from different types of sensors, the data may be dissimilarfrom one sensor to the next. Also, the data may be collected atdifferent times and/or sampling rates. The large amount of data comingfrom various sources complicates the task of reconstructing theinformation for a display while preserving the correct relationshipsamong the data.

SUMMARY

This document discusses, among other things, systems and methods forinterpreting data collected from physiologic sensors. A system exampleincludes a medical device that includes a processor and a Z-scoretransformation (ZST) module included in the processor. The system alsoincludes a display in communication with the processor. The processor isadapted to receive sensor data obtained from at least first and secondsensors adapted to produce a time-varying physiologic electrical sensorsignal. At least one of the first and second sensors is implantable. TheZST module calculates a ZST for the sensor data received from the firstsensor and a ZST for the sensor data received from the second sensor.The display is adapted to display the ZSTs in visual correspondence witheach other over time.

A method example includes sensing at least one time-varying physiologicimplantable sensor signal, sampling the sensor signal to obtain sensordata, calculating a multi-resolution ZST of the sensor data, anddisplaying a resulting multi-resolution ZST. A multi-resolution ZST iscalculated by calculating a ZST over a first data segment length of asensor signal to obtain a first resolution ZST region, and calculatingat least a second ZST over a second data segment length of the sensorsignal to obtain at least a second resolution ZST region.

This summary is intended to provide an overview of the subject matter ofthe present patent application. It is not intended to provide anexclusive or exhaustive explanation of the invention. The detaileddescription is included to provide further information about the subjectmatter of the present patent application.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of portions of a system that uses animplantable medical device (IMD).

FIG. 2 illustrates an IMD coupled by one or more leads to a heart.

FIG. 3 is a block diagram of portions of an example of a system forinterpreting data collected from physiologic sensors and other sources.

FIG. 4 shows graphs of an example of displaying Z-score transformations(ZSTs) for sensor data.

FIG. 5 shows an example of a display of a graph of a ZST of a sensorsignal and a color-coded region.

FIG. 6 shows an example of a display of graphs of multi-resolution ZSTsobtained from signals from four sensors and a color map.

FIG. 7 shows a flow diagram of an example of a method to interpret dataobtained from physiologic sensors.

FIG. 8 shows a flow diagram of another example of a method to interpretdata obtained from physiologic sensors.

FIG. 9 shows a flow diagram of an example of a method of calculating aZST offline.

FIG. 10 shows a flow diagram of an example of a method of calculating aZST in real time.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings which form a part hereof, and specific embodimentsin which the invention may be practiced are shown by way ofillustration. It is to be understood that other embodiments may be usedand structural or logical changes may be made without departing from thescope of the present invention.

This document discusses systems and methods for interpreting datacollected from physiologic sensors. In a multi-sensor multi-modalphysiologic detection system, a large amount of data is acquired from avariety of sensors and other sources. Such a system should be able totransmit, monitor, replay, and trend signals from different sources, anddetect events of interest from the signals.

A Z-score transformation of a variable X having mean μ, standarddeviation σ, and variance σ² is defined as

Z _(X):=(X−μ)/σ  (1)

and indicates how far and in what direction X deviates from its mean,expressed in units of its standard deviation. Because Z_(X) is zero-meanand unit-variance regardless of the statistical property of X, ZST canbe very useful in such a system as it makes it easier to compare therelative standings of parameters from distributions with different meansand/or different variances, to identify quickly the correlation amongdifferent signals, and to recognize the outstanding spatial or temporalregions where an event of interest may occur. ZST with multipleresolutions further facilitates quick recognition of the outstandingtrend and/or the inter-signal relationship.

FIG. 1 is a block diagram of portions of a system 100 that uses animplantable medical device (IMD) 110. As one example, the system 100shown is used to treat a cardiac arrhythmia. The IMD 110 typicallyincludes an electronics unit coupled by a cardiac lead 108, oradditional leads, to a heart 105 of a patient 102, or otherwiseassociated with the heart 105. Examples of IMD 110 include, withoutlimitation, a, pacemaker, a cardioverter, a defibrillator, a cardiacresynchronization therapy (CRT) device, and other cardiac monitoring andtherapy delivery devices, including cardiac devices that include or workin coordination with neuro-stimulating devices, drug pumps, or othertherapies. System 100 also typically includes an IMD programmer or otherexternal device 170 that communicates wireless signals 160 with the IMD110, such as by using radio frequency (RF) or other telemetry signals.

Cardiac lead 108 includes a proximal end that is coupled to IMD 110 anda distal end, coupled by an electrode or electrodes to one or moreportions of a heart 105. The electrodes typically deliver cardioversion,defibrillation, pacing, or resynchronization therapy, or combinationsthereof to at least one chamber of the heart 105. The electronics unitof the IMD 110 typically includes components that are enclosed in ahermetically-sealed canister or “can.” Other electrodes may be locatedon the can, or on an insulating header extending from the can, or onother portions of IMD 110, such as for providing pacing energy,defibrillation energy, or both, in conjunction with the electrodesdisposed on or around a heart 105. The lead 108 or leads and electrodesmay also typically be used for sensing intrinsic or other electricalactivity of the heart 105.

FIG. 2 illustrates an IMD 110 coupled by one or more leads 108 to heart105. Heart 105 includes a right atrium 200A, a left atrium 200B, a rightventricle 205A, a left ventricle 205B, and a coronary sinus 220extending from right atrium 200A. Lead 108 includes electrodes(electrical contacts, such as ring electrode 225 and tip electrode 230)disposed in a ventricle 205A of heart 105 for sensing signals, ordelivering pacing therapy, or both, to the ventricle 205A. Lead 108 alsoincludes one or more electrodes for placement in the right atrium 200A,such as ring electrode 235 and ring electrode 240, for sensingelectrical cardiac signals, delivering pacing therapy, or both sensingsignals and delivering pacing therapy. Sensing and pacing allows the IMD110 to adjust timing of the chamber contractions. For example, IMD 110can adjust the timing of ventricular contractions with respect to thetiming of atrial contractions delay by sensing a contraction in theright atrium 200A and pacing the right ventricle 205A at the desired AVdelay time. The IMD also includes an electrode formed on the IMD can250, and a header electrode formed on the IMD header 255.

IMD optionally also includes additional leads and electrodes, such asfor delivering atrial cardioversion, atrial defibrillation, ventricularcardioversion, ventricular defibrillation, or combinations thereof toheart 105. Such electrodes typically have larger surface areas thanpacing electrodes in order to handle the larger energies involved indefibrillation. Optionally, lead 108 includes two leads containing twoelectrodes each. In an example, a first lead includes a tip electrodelocated in the apex of the right ventricle 205A and a first ringelectrode located proximal to the tip electrode. A second lead includesa tip electrode located in the right atrium 200A and a ring electrodelocated in the right atrium 200A proximal to the tip electrode.

Optionally, IMD 110 includes an additional cardiac lead that includesring electrodes for placement in a coronary vein extending along a wallof the left ventricle 205B. A lead placed in the left ventricle 205B anda lead placed in the right ventricle 205A may be used to optionallyprovide resynchronization therapy to the heart 105.

Other forms of electrodes include meshes and patches which may beapplied to portions of heart 105 or which may be implanted in otherareas of the body to help “steer” electrical currents produced by IMD110. The present methods and systems will work in a variety ofconfigurations and with a variety of electrodes.

FIG. 3 is a block diagram of portions of an example of a system 300 forinterpreting data collected from physiologic sensors and other sources.The system 300 includes a medical device 305. The medical device 305includes a processor 315 that receives sensor data obtained from atleast first and second sensors. The term processor may include a digitalsignal processor, ASIC, microprocessor, or other type of processor. Insome examples, the medical device 305 is an external medical device thatcommunicates with an implantable medical device (IMD) usingcommunication circuit 310. In some examples, the medical device 305 isan IMD programmer. In some examples, the medical device 305 is part of apatient management system, which may include a remote server thatcommunicates over a communication network, such as a mobile phonenetwork, or a computer network with a local external interface to animplantable or personal ambulatory medical device. In some examples, themedical device 305 includes a server in communication with a networksuch as a hospital computer network or the internet.

Each sensor produces a time-varying physiologic electrical sensorsignal. At least one of the sensors is an implantable sensor. Anon-exhaustive list of examples of implantable sensors include anaccelerometer, an implantable pressure sensor, an intracardiac impedancesensing circuit, a transthoracic impedance sensing circuit, anelectrical cardiac signal sensing circuit, a blood pressure sensor, anda heart sound sensor. Examples of external sensors include, among otherthings, an external ECG circuit and an external blood pressure sensor.The external sensors may be coupled to the medical device 305 or to asecond external device in communication with the first medical device305.

The processor 315 includes a Z-score transformation (ZST) module 320.The ZST module 320 includes software, hardware, firmware or anycombination of software, hardware, and firmware. The ZST module 320calculates a ZST for the sensor data received from the first sensor anda ZST for the sensor data received from the second sensor. The system300 also includes a display 325 in communication with the processor 315to display the ZSTs in visual correspondence with each other over time.In some examples, the display 325 is coupled to the processor 315, suchas with an IMD programmer, or can be in communication with the processor315 over a network, such as in a patient management system. For example,the processor 315 can be included in a remote server system incommunication with a second device over a network, and the second deviceincludes the display 325.

FIG. 4 shows graphs 400 of an example of displaying ZSTs for sensordata. The top graph 405 displays sensor data 410 (darker line) receivedfrom an implantable accelerometer (XL) sensor and sensor data 415(lighter line) received from an implantable transthoracic impedancesensor used to measure respiration, such as minute ventilation (MV). Theaccelerometer produces an electrical sensor signal representative ofmotion or activity level of a patient. The transthoracic impedancesensor produces an electrical sensor signal representative of breathingof the patient and is useful to detect changes in breathing, such as dueto exercise, stress, and emotion.

Typically, the “raw” data from sensors is recorded and played back fordisplay. Often it can be difficult for a caregiver to visually inspectthe display and quickly recognize the relative importance of the sensoroutputs or to recognize any exceptional value of one or both sensors.The scale of the raw data obtained from the two sensors may differ andthe sensors may have different signal statistics as well, such as adifferent mean, variance, or probability distribution. In the top graph405 for example, the scale of the XL sensor data is in milli-g's (where1 g=acceleration of gravity) and the scale of the MV data is in counts.The middle graph 420 displays the Z-score transform (ZST) for the XLdata (Z_(XL)) 425 and the ZST for the MV data (Z_(MV)) 430. The ZSTsnormalize the data from the sensors to the same scale so that thecomparison becomes more meaningful. The ZST is typically expressed inunits of standard deviations (SD). The normal value range of the ZST canbe defined as the mean±2 SD. The two horizontal bars 435, 440 indicatethe normal value range. The region outside the normal value range issometimes called the outstanding region to indicate sample values thatare away from the mean.

The ZST for a time series of sampled data x(n) can be computed on asegment-by-segment basis. This can sometimes be called a segmental ZST.The system 300 acquires data from the sensors. In some examples, thedata is collected by the IMD and communicated to the medical device 305.The data can be collected and communicated in real time, or the IMD cancollect the data, store it in an IMD memory, and communicate the data ata later time. In some examples, the medical device 305 is part of apatient management system and includes a remote server. The data iscommunicated by the IMD or a second external device to the remoteserver. T-hours of data is collected and stored. The ZST of that T-hourdata segment is performed by the ZST module 320 and displayed, such asin graph 420. The next segment is then processed. In some examples, theZST module 320 automatically updates a ZST calculation when new sampledsensor data becomes available from a sensor.

Segmental ZST is affected by the size of the data segment from which thesample mean μ and variance σ² are computed. For the same number of datapoints, a longer data segment will yield a lower-resolution estimate ofμ and σ², and conversely, a shorter data segment will yield ahigher-resolution estimate of μ and σ². A multi-resolution ZST can beconceptualized as the calculation of ZSTs over multiple segment lengthsT of the data. This can be seen in the bottom two graphs 420 and 445 ofFIG. 4. Graph 445 is an expanded view of the data segment 450 in graph420 (shown by the broken line). If a ZST is calculated over the segmentshown in graph 445 (shorter T) using the same number of data points asis used to calculate a ZST for the entire graph 420 (longer T), μ and σ²for the bottom graph 445 will have a higher resolution, and willtherefore reflect more of the small changes shown in graph 445 that arenot apparent in graph 420.

In some examples, the ZST module 320 is adapted to determine a ZSThaving a first resolution for a first segment of sensor data obtainedfrom a sensor, and to determine a ZST having a second resolution for asecond segment of sensor data obtained from the sensor. In someexamples, the first segment is included in a different segment of sensordata than the second segment. In some examples, the first and seconddata segment is included in the same data segment.

In some examples, a multi-resolution ZST is determined by the ZST module320 automatically when it is determined that a ZST is outside of apredetermined normal range. For example, in graph 420, the segment 450includes an outstanding region having a Z score that exceeds two, andthe ZST module automatically calculates a higher resolution ZST over thesegment 450. The multi-resolution ZST may be calculated when the ZST hasa predetermined threshold number of points outside of the normal range.

In some examples, a multi-resolution ZST is determined interactively.Returning to FIG. 3, the system 300 includes a user interface 330 incommunication with the processor 315. Examples of a user interface 330include without limitation, a computer mouse, a keyboard, atouch-screen, or a graphical user interface (GUI). A ZST can be firstcalculated and displayed for the first segment under a coarse resolutionT₀ designated by the user. The ZST module 320 then calculates a ZST overthe second segment in response to a signal received through the userinterface 330. An example is shown in the bottom two graphs 420 and 445of FIG. 4. The user selects a region of interest 450 in the firstsegment.

In response to the user's selection, the ZST module 320 automaticallycalculates and displays the ZST of the interest region 450 under a finerresolution T₁, where T₁<T₀. Graph 445 is the expanded view of the areaof interest 450 with greater resolution. This can be viewed as “zoomingin” on an indicated area of interest. A general multi-resolution ZST canbe computed using the resolutions {T_(o)/2^(i)}_(i=1˜N), where T₀ is theinitial coarse resolution, i.e. i resolutions or levels are calculatedfor the region of interest 450 based on the user's selection.

In some examples, the display 325 is a color display and the processor315 includes a display module 335. The regions of the ZST are dividedbased on whether a region is within a predetermined value range (e.g.,±1SD) or is outside the predetermined value range. FIG. 5 shows anexample of a display 500 of a graph 505 of a ZST of a sensor signal anda color-coded region 510 to indicate different value regions of Zscores. The display module 335 assigns a first display color-code 515 toone or more regions 517 of the ZST that are within the range|Z_(x)|<1SD, assigns a second display color-code 525 to one or moreregions 527 of the ZST that are within the range 1SD<|Z_(x)|<2SD, andassigns a third display color-code 520 to one or more regions 522 of theZST that are outside the range |Z_(x)|>2SD. More than three ranges maybe used. In some examples, only two ranges are used: a first color-codeto indicate that the ZST is within a predetermined normal value range(e.g., within ±2SD) and a second color-code to indicate that the ZST isoutside the predetermined normal value range (i.e., in an outstandingregion). A ZST may have to have a threshold number of points outside ofa value range before a second region is identified and a color codeassigned.

FIG. 6 shows an example of a display of graphs 605 of ZSTs obtained fromsignals from four sensors and a color map 610. The sensor signals mayhave different scales with respect to time and/or data. The ZST module320 calculates the ZSTs. The display module 335 assigns color codes 615,625, and 620 to regions having Z-scores where |Z_(x)|<1, 1<|Z_(x)|<2,and |Z_(x)|>2, respectively, to form the color map 610. In someexamples, the display module 335 displays different combinations of theZSTs according to input received from the user interface 330. The graphs605 of the four ZSTs of sensor data may be a subset selected by a userof the set of sensors for which data was obtained. Or a user can selecta subset of the four ZSTs shown using the user interface 330.

In some examples, the display module 335 assigns a first displaycolor-code to a ZST region having a first resolution and a seconddisplay color-code to a ZST region having a second resolution. In someexamples, the color-codes are displayed on a color map region that isseparate from the ZST graph, such as region 510 in FIG. 5. In someexamples, color-codes are displayed on the graph 505 of data, althoughthis makes it more difficult to notice the different color regions. Thedisplay module 335 assigns a first display color-code to one or moreregions of the ZST having a first resolution and a second color-code toone or more regions of the ZST region having a second resolution. Ifthere are i resolutions calculated for the ZST, the display module canassign i color-codes to indicate the regions.

In some examples, the ZST module 320 calculates multi-resolution ZSTsfor sensor data obtained from a plurality of sensor signals. One sensorsignal may have a different scale from one or more of the other sensorsignals with respect to time, or with respect to the data, or withrespect to both time and data. The display module 335 assignscolor-codes to regions of the ZSTs according to ZST resolution. Thedisplay module 335 displays both the ZSTs of the sensor signals and acolor map to indicate regions of multiple resolution of the ZST. In someexamples, a different resolution for each of the sensor signals 605 canbe used in the display. The display module 335 may automatically createa color-coded map for the sensor signals 605 to indicate the multipleresolutions.

In some examples, the system 300 includes a memory in electricalcommunication with the processor 315 to store sensor data obtained froma plurality of sensor signals over at least one circadian cycle of thesubject. The ZST module 320 calculates multi-resolution ZSTs over thesensor data obtained over the circadian cycle. This is useful to displayZSTs to show any variations of the sensor signals with circadianrhythms. For example, such a display may show that one or more sensorsignals enter an outstanding region at a certain time of day.

According to some examples, the processor 315 includes a trendingmodule. Sensor data received from one or more sensors and is stored in amemory. The trending module trends the sensor data and the ZST module320 calculates a ZST of the trended sensor data. This is useful toidentify physiologic cardiovascular events that may take some time todevelop, such as heart failure decompensation for example. A ZST can becalculated over data for one or more sensors collected over days. A usercan then zoom in on specific data segments through the user interface.This allows trend to be developed over prolonged periods such as aweek-long period for example.

FIG. 7 shows a flow diagram of an example of a method to interpret dataobtained from physiologic sensors. At 705, at least one time-varyingphysiologic implantable sensor signal is sensed. At 710, the sensorsignal is sampled to obtain sensor data. A multi-resolution ZST is thencalculated for the sensor data. To calculate the multi-resolution ZST, aZST is calculated over a first data segment length of a sensor signal toobtain a first resolution ZST region at 715, and at least a second ZSTis calculated over a second data segment length of the sensor signal toobtain at least a second resolution ZST region at 720. At 725, aresulting multi-resolution ZST is displayed. In some examples, thesecond resolution ZST is calculated ZST when prompted through a userinterface.

In some examples, the method 700 includes assigning a first displaycolor-code to the first resolution ZST region and a second displaycolor-code to the second resolution ZST region. Both the ZST of a sensorsignal and one or more color-codes to indicate regions of multipleresolution of the ZST are displayed.

In some examples, a plurality of time-varying physiologic sensor signalsis sensed. Multi-resolution ZSTs for the plurality of sensor signals arecalculated and color-codes are assigned to multi-resolution regions ofthe ZSTs. Both the ZSTs of the sensor signals and a color map toindicate regions of multiple-resolution of the ZSTs are displayed.

In some examples, the method 700 includes assigning a first displaycolor-code to a first region of the ZST and assigning a second displaycolor-code to a second region of the ZST. The second region of the ZSTincludes at least one sensor data value that is outside of apredetermined normal value range. In some examples, the predeterminednormal value range is ±2 standard deviations. Both the ZST and one ormore color-codes are displayed to indicate one or more regions having avalue outside the predetermined normal value range.

FIG. 8 shows a flow diagram of another example of a method to interpretdata obtained from physiologic sensors. At 805, a first time-varyingphysiologic sensor signal and at least a second time-varying physiologicsensor signal are sensed. In some examples, first sensor signal has adifferent scale from the second sensor signal with respect to time. Insome examples, first sensor signal has a different scale from the secondsensor signal with respect to data. In some examples, time-varyingphysiologic sensor signals are sensed from two or more sensors, and thesensor signals are related to physiologic cardiovascular events of asubject selected from, among other things, mean arterial pressure,stroke volume, heart rate, respiration rate, respiration volume,mechanical vibrations of the subject's heart, and activity of thesubject.

At 810, the first and second sensor signals are sampled to obtain sensordata. At 815, a Z-score transformation (ZST) of the sensor data of thefirst sensor signal and a ZST of the sensor data of the second sensorsignal are calculated. In some examples, a ZST is calculated over asufficient amount of sensor data to record circadian variance of asensor signal. In some examples, a ZST is recalculated in response to asignal received from a user interface. In some examples, a ZST isrecalculated automatically when new sampled sensor data is available.

At 820, the ZSTs are displayed in visual correspondence with each otherover time. In some examples, a first display color-code is assigned to afirst region of a ZST and a second display color-code is assigned to asecond region of the ZST. The second region of the ZST includes one ormore sensor data values that are outside of a predetermined normal valuerange. The ZST of the sensor signal is displayed together with one ormore color-codes to indicate one or more regions having a value outsidethe normal value range. In some examples, the method 800 includestrending the sensor data of at least one of the first and second sensorsignals, calculating the ZST of trended sensor data, and displaying atleast one ZST of the trended sensor data.

In some examples, the sensor data is collected by an IMD and data iscommunicated to an external device at a later time. The ZST iscalculated after all of the data is communicated. This can be referredto as an off-line ZST calculation. FIG. 9 shows a flow diagram of anexample of a method 900 of calculating a ZST offline. At 905 a time isinitialized. The timer times a duration for collecting data segment Xwhich begins at 910. When the timer expires at 915, Z(X) is computed at920 using Equation (1). The timer is cleared at 925 and the method 900may resume collecting data or may wait for a signal generated by adetected event or a user before proceeding.

In some examples, the sensor data can be collected and communicated toan external device in real time. The ZST can be calculated as databecomes available. This can be referred to as a real time ZSTcalculation or a sequential ZST calculation. FIG. 10 shows a flowdiagram of an example of a method 1000 of calculating a ZST in realtime. At 1005, an initial segment of data is collected. At 1010, themean μ and standard deviation σ and a ZST for the segment are computed.If the standard deviation σ is below a predetermined threshold T at1015, the mean μ and standard deviation σ are initialized with thecomputed values at 1020. The threshold check ensures that the initialvalues are not calculated with outlier values. At 1025, one data inputx(i) is collected when it becomes available. At 1030, the ZST iscomputed using x(i) according to

Z _(X)(i):=(x(i)−μ)/σ  (2).

At 1035, it is determined if the computed ZST is greater than apredetermined threshold k. Typically k is equal to two standarddeviations, or k=2σ. If ZST is less than k, then new values for the meanμ and standard deviation σ are calculated at 1040. Otherwise, the indexi is incremented at 1045 and the next data value is obtained at 1025.Also at 1040, the mean μ is calculated according to

μ=αμ_(o)+(1−α)x(i)  (3),

and the variance σ² is calculated according to

σ²=βσ_(o) ²+(1−β)[x(i)−μ]²  (4).

The running values of μ_(o) and σ_(o) are updated with the calculatedvalues at 1045. The index i is incremented at 1045 and the next datavalue is obtained at 1025.

The examples of the methods described herein can be implemented insoftware. The software comprises computer executable instructions storedon computer readable media such as memory or other type of storagedevices. This includes remote storage devices where the instructions aredownloadable via the internet or other network to a machine forprocessing. Further, such methods may correspond to modules, which aresoftware, hardware, firmware or any combination thereof. Multiplefunctions are performed in one or more modules as desired, and theembodiments described are merely examples. The software is executed on adigital signal processor, ASIC, microprocessor, or other type ofprocessor, such as in a personal computer, server or other computersystem.

The accompanying drawings that form a part hereof, show by way ofillustration, and not of limitation, specific embodiments in which thesubject matter may be practiced. The embodiments illustrated aredescribed in sufficient detail to enable those skilled in the art topractice the teachings disclosed herein. Other embodiments may beutilized and derived therefrom, such that structural and logicalsubstitutions and changes may be made without departing from the scopeof this disclosure. This Detailed Description, therefore, is not to betaken in a limiting sense, and the scope of various embodiments isdefined only by the appended claims, along with the full range ofequivalents to which such claims are entitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations, or variations, or combinations of variousembodiments. Combinations of the above embodiments, and otherembodiments not specifically described herein, will be apparent to thoseof skill in the art upon reviewing the above description.

The Abstract of the Disclosure is provided to comply with 37 C.F.R. §1.72(b), requiring an abstract that will allow the reader to quicklyascertain the nature of the technical disclosure. It is submitted withthe understanding that it will not be used to interpret or limit thescope or meaning of the claims. In addition, in the foregoing DetailedDescription, it can be seen that various features are grouped togetherin a single embodiment for the purpose of streamlining the disclosure.This method of disclosure is not to be interpreted as reflecting anintention that the claimed embodiments require more features than areexpressly recited in each claim. Rather, as the following claimsreflect, inventive subject matter lies in less than all features of asingle disclosed embodiment. Thus the following claims are herebyincorporated into the Detailed Description, with each claim standing onits own.

1. A method comprising: sensing at least one time-varying physiologicsensor signal using an implantable sensor; sampling the sensor signal toobtain sensor data; calculating a multi-resolution Z-scoretransformation (ZST) of the sensor data by: calculating a ZST over afirst data segment length of a sensor signal to obtain a firstresolution ZST region; and calculating at least a second ZST over asecond data segment length of the sensor signal to obtain at least asecond resolution ZST region, wherein at least a portion of the secondsegment overlaps the first segment, and wherein the second resolution iscapable of being different than the first resolution; and displaying aresulting multi-resolution ZST.
 2. The method of claim 1, including:assigning a first display color-code to a first region of the ZST;assigning a second display color-code to a second region of the ZST,wherein the second region of the ZST includes a sensor data value thatis outside of a predetermined normal value range; and displaying boththe ZST of a sensor signal and one or more color-codes to indicate oneor more regions having a value outside the predetermined normal valuerange.
 3. The method of claim 2, wherein the predetermined normal valuerange is two standard deviations.
 4. The method of claim 1, including:assigning a first display color-code to the first resolution ZST regionand a second display color-code to the second resolution ZST region; anddisplaying both the ZST of a sensor signal and one or more color-codesto indicate regions of multiple resolution of the ZST.
 5. The method ofclaim 4, including: sensing a plurality of time-varying physiologicsensor signals; calculating multi-resolution ZSTs for the plurality ofsensor signals; assigning color-codes to multi-resolution regions of theZSTs; and displaying both the ZSTs of the sensor signals and a color mapto indicate regions of multiple-resolution of the ZSTs.
 6. The method ofclaim 1, wherein calculating a multi-resolution Z-score transformation(ZST) includes: calculating a ZST over a first data segment length of asensor signal to obtain a first resolution ZST; and calculating at leasta second ZST over a second data segment length of the sensor signal toobtain at least a second resolution ZST when prompted through a userinterface.
 7. The method of claim 1, wherein calculating amulti-resolution ZST includes calculating a multi-resolution ZST whenthe processor determines that the ZST has a predetermined number ofpoints outside a normal range.
 8. The method of claim 1, whereinsampling the sensor signal to obtain sensor data occurs in real time andwherein calculating a ZST includes calculating the multi-resolution ZSTwhile the sensor data is being obtained.
 9. The method of claim 8,wherein calculating the ZST includes calculating a ZST Z_(X)(i) in realtime, when a new sensor datum x(i) is obtained, according toZ _(X)(i):=(x(i)−μ)/σ.
 10. A method comprising: sensing a firsttime-varying physiologic sensor signal and sensing at least a secondtime-varying physiologic sensor signal, wherein at least one of thefirst and second sensor signals is obtained from an implantable sensor;sampling the first and second sensor signals to obtain first sensor dataand second sensor data; calculating a Z-score transformation (ZST) forthe first sensor data and a ZST for the second sensor data including:calculating a ZST over a first data segment length of at least one ofthe first or second sensor data to obtain a first resolution ZST region;and calculating at least a second ZST over a second data segment lengthof the same first or second sensor data to obtain at least a secondresolution ZST region wherein at least a portion of the second segmentoverlaps the first segment, and wherein the second resolution is capableof being different than the first resolution; and displaying the ZSTs invisual correspondence with each other over time.
 11. The method of claim10, including: assigning a first display color-code to a first region ofa ZST; assigning a second display color-code to a second region of theZST, wherein the second region of the ZST includes a sensor data valuethat is outside of a predetermined normal value range; and displayingboth the ZST of a sensor signal and one or more color-codes to indicateone or more regions having a value outside the predetermined normalvalue range.
 12. The method of claim 10, including: trending the sensordata of at least one of the first and second sensor signals; calculatingthe ZST of trended sensor data; and displaying at least one ZST of thetrended sensor data.
 13. The method of claim 10, including sensing aplurality of time-varying physiologic sensor signals, wherein theplurality of sensor signals are related to physiologic cardiovascularevents of a subject selected from the group consisting of: mean arterialpressure; stroke volume; heart rate; respiration rate; respirationvolume; mechanical vibrations of the subject's heart; and activity ofthe subject.
 14. The method of claim 10, wherein calculating a ZSTincludes recalculating a ZST when new sampled sensor data is available.15. The method of claim 10, wherein calculating a ZST includescalculating a ZST over a sufficient amount of sensor data to recordcircadian variance of a sensor signal.
 16. The method of claim 10,wherein sensing the first and second sensor signals includes sensing afirst sensor signal having a different scale from the second sensorsignal with respect to time.
 17. The method of claim 22, wherein sensingthe first and second sensor signals includes sensing a first sensorsignal having a different scale from the second sensor signal withrespect to data.
 18. The method of claim 10, wherein displaying the ZSTsin visual correspondence with each other over time includes displayingdifferent combinations of the ZSTs according to input received fromthrough a graphical user interface.
 19. The method of claim 10, whereindisplaying the ZSTs in visual correspondence with each other over timeincludes: assigning a first display color-code to a ZST region havingthe first resolution and a second display color-code to a ZST regionhaving the second resolution; and displaying the ZST of a sensor signaland indicate regions of multiple resolution of the ZST using thecolor-codes.
 20. The method of claim 10, wherein displaying the ZSTs invisual correspondence with each other over time includes displaying boththe ZSTs of the sensor signals and a displaying color map to indicateregions of multiple resolution of the ZST.